4 Reasons to Use AI Developed for Market Research

Artificial intelligence (AI) is generating tremendous value in an increasing number of fields including media, communication, logistics, and finance. However, it appears that market research has not yet joined this trend.

Is automation completely absent from market researchers’ toolkits? Of course not. It is, however, expected to save a large portion of money and time. It’s automation in its simplest form: doing repetitive and easy tasks faster than humans.

When it comes to more explorative and qualitative work, despite the progress of AI in data extraction and interpretation, market researchers tend to favor offline traditional methodologies (e.g focus groups). Why is that? Are market researchers skeptical about AI despite its proven effectiveness in other fields? Perhaps. Having said that, many are still waiting for clear evidence of robust and trusted insights.

There are several reasons that should encourage market research practitioners to embrace Artificial Intelligence.

Reason 1: Less consumer biases

Traditional approaches (e.g focus groups or online questionnaires) are biased as they rely on human memory, which can be faulty or may unconsciously interpret past events. No matter how well your moderator leads interviews or how well a questionnaire is designed, the information you collect will still be the result of what consumers say.

AI-powered market research can solve this problem. It can dive into pieces of consumers’ lives left online and retrace several months, sometimes years, of online activity. The detail of this kind of analysis can be stunning: what they did, where, with whom, when and why.

Reason 2: Superior recruitment capabilities

Typical consumer panels are very effective at targeting consumers with general or common needs. The moment the field narrows to specific needs and attributes, however (e.g. parents going on holiday with kids in a specific region of Ireland), panels quickly show their limitations. Accomplishing work in these narrowed search fields will either be impossible or will require large amounts of additional time and budget to reach a limited number of consumers.

AI-powered tools have the ability to build rich profiles and to aggregate a panel of consumers almost instantaneously and with very little time or money spent on human effort.

Reason 3: Extensive traceability

Once we create the consumer target, ideally we would like to know everything about the consumer experience: what they did, where they visited, which monuments did they see, at what time, in which order and why. The best moderator/questionnaire could get 70% of what happened but mostly at a surface level. Why is this?

It is because human memory is a faulty tool, prone to leaping to conclusions, missing information, forgetting things, etc. The consequences of this when applied to marketing research are that:

· Exact times will never be accurate

· Less known interest points might not be considered interesting or noteworthy

· Activity variety for each place will only be analyzed on the surface

Reason 4: Real-time insights

Traditional market research tools need to conduct several steps of analysis before they can deliver insights. Thus delivering real-time insights seems like a far-away dream. Some online tools, such as social listening tools, manage to reach real-time results but are inefficient for exploratory purposes.

Once set-up, an AI market research tool enriches insights by continuously aggregating data. If you are operating in the travel and hospitality industry you don’t have to to wait for the holiday season to be over to generate insights. Instead, you will be able to follow your consumers and understand their aspirations and drives along the journey, giving you the capacity to act in real time too.

Does this mean all traditional methodologies are obsolete? Of course not. Artificial intelligence can unlock amazing amounts of information and has a spectacular array of features and capabilities. However, AI-powered market research is not the holy grail and it has its own limitations. The same thing applies to focus groups or consumer interviews. These older methods of analysis can still be used with great effectiveness to complement the information gathered through AI.

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